Service Mesh
A service mesh is an infrastructure layer that manages service-to-service communication in a microservices architecture. It provides traffic management, security, observability, and reliability features without requiring changes to application code.
Why Use a Service Mesh?
Traffic Management: Fine-grained control over routing, retries, timeouts, and circuit breaking
Security: mTLS encryption, service authentication, and policy enforcement
Observability: Distributed tracing, metrics, and logging for all service traffic
Reliability: Automatic retries, failover, and health checks
Zero-Trust Networking: Enforce least-privilege and secure-by-default communication
Pros and Cons
Enhanced security (mTLS, RBAC)
Added complexity and resource overhead
Consistent traffic policies
Steep learning curve for teams
Deep observability and tracing
May impact latency/performance
Platform-agnostic (multi-cloud)
Debugging can be harder
Enables progressive delivery (canary, blue/green)
Popular Service Mesh Providers
Istio (open source, works on any Kubernetes, supported by GKE, AKS, EKS)
Linkerd (lightweight, easy to install, CNCF project)
Consul Connect (HashiCorp, integrates with VMs and Kubernetes)
AWS App Mesh (managed for EKS, ECS, EC2)
Azure Service Mesh (preview, managed for AKS)
Anthos Service Mesh (GCP, managed Istio)
Example: Installing Istio on Kubernetes (Cloud-Agnostic)
For AKS: Use Azure CLI to create the cluster, then follow the above steps
For EKS: Use AWS CLI and eksctl to create the cluster, then follow the above steps
For GKE: Use gcloud to create the cluster, then follow the above steps
Example: Deploying a Sample App with Istio
Access the app via Istio ingress gateway (see Istio docs for cloud-specific instructions).
Example: Enabling mTLS for All Services
Best Practices (2025)
Start with a minimal mesh (e.g., Linkerd or Istio demo profile) and scale up
Use GitOps (ArgoCD, Flux) to manage mesh configuration and CRDs
Monitor mesh health with Prometheus, Grafana, and Jaeger
Use LLMs (Copilot, Claude) to generate and review mesh policies and manifests
Document mesh usage and onboarding for your team
Common Pitfalls
Overcomplicating the mesh with too many features at once
Not monitoring mesh resource usage (can impact cluster performance)
Failing to secure the mesh dashboard and control plane
Manual changes outside Git (causes drift in GitOps setups)
References
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